Biblio

The research try to implements an intelligent network operation management system for enterprise networks. First, based on Flask-state software architecture, the system adapt to Phytium CPU and Galaxy Kylin operating system successfully. Second, using the Isolation Forest algorithm, the system implements the anomaly detection of host data such as CPU usage. Third, using the Holt-winters seasonal prediction model, the system can predict time series data such as network I/O. The results show that the system can realizes anomaly detection and time series data prediction more precisely and intelligently.

This paper describes the technology of neural network application to solve the problem of information security incidents forecasting. We describe the general problem of analyzing and predicting time series in a graphical and mathematical setting. To solve this problem, it is proposed to use a neural network model. To solve the task of forecasting a time series of information security incidents, data are generated and described on the basis of which the neural network is trained. We offer a neural network structure, train the neural network, estimate it's adequacy and forecasting ability. We show the possibility of effective use of a neural network model as a part of an intelligent forecasting system.